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RealPage Underwriting Analytics Delivers a Smarter Answer Faster

The process of deciding whether to invest in properties hasn’t changed much in decades. Your internal analysts (or highly-paid outside consultants) gather and crunch numbers about property performance history, rent and sales comps, market forecasts, competition, seasonal exposure and more to make an informed decision. This can take weeks – time better spent looking at and selecting from a lot more deals. After all, the more deals you’re able to look at, the better your chances of seeing the best ones.

Underwriting Analytics combines property data with data science

There are two critical components to reducing risk and making the right decision. First, there’s property data itself (some examples are provided above). But there’s also data science– the manipulation of the data necessary to turn it into actionable information. And until now, there’s been no solution for property investors that aggregates the best data available and pairs it with the latest data science (including artificial intelligence and machine learning).

RealPage Underwriting Analytics represents a watershed in decision support for investors, brokers, lenders, appraisers and others whose jobs involve prospective acquisitions. The concept behind this solution was to combine the gold standards for both property data and data science into an on-demand tool for getting instant answers to the following questions:

What is this asset worth?

What are the real comps?

How has this asset performed?

How will this asset perform in the future?

Should I buy a plane ticket to chase this deal?

RealPage’s survey-based rents and lease transaction data have long been considered the industry’s deepest and most accurate, and sales comps are delivered through a partnership with market leader Real Capital Analytics. Together they constitute the most reliable source of property performance data available anywhere. And importantly, it’s granular down to the street corner level – since a submarket can change quickly from one street to the next.

Complimentary access to property performance metrics

In conjunction with the launch of Underwriting Analytics, RealPage is providing complimentary access to highly valuable market and property performance metrics including market and property level asking rent, effective rent, rent concessions and occupancy for millions of units.

The artificial intelligence, machine learning and proprietary algorithms underlying the analysis engine are provided by (company), whose other clients include Amazon, NASA and other data-obsessed institutions. The system also leverages the predictive analytics of YieldStar and LRO. So the data science component of RealPage Underwriting Analytics is as peerless as the data itself.

While it’s true that this solution can replace weeks of painstaking data collection and analysis, providing answers in mere seconds at the click of a mouse, it’s not a “black box” that does things its way or no way at all. Rent comps, sales comps and construction pipelines are configurable to reflect your investment goals and strategies, personal expertise and inside knowledge. You can also continue to use your current underwriting models if you wish, importing data from the Underwriting Analytics into your Excel-based or other models.

On-demand access for simplicity

RealPage Underwriting Analytics doesn’t require an expensive subscription or training sessions. It’s available on-demand: you tap into it as needed when presented with opportunities. It’s a perfect embodiment of RealPage’s “North Star” commitment to innovation and simplicity: an innovation that replaces the complexity of data gathering and analysis in support of investment decisions with the simplicity of an on-demand “wizard” armed with unsurpassed information and brainpower.

Based in New Orleans, Guy Lyman is a professional writer with over 25 years’ experience writing about multifamily and commercial real estate. Lyman is a frequent contributor and writer for the Property Management Insider blog.

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